Development and Comparability of a Short Food-Frequency Questionnaire to Assess Diet in Prostate Cancer Patients: The Role of Androgen Deprivation Therapy in CArdiovascular Disease – A Longitudinal Prostate Cancer Study (RADICAL PC) Substudy
Bibliographic record
Abstract
BACKGROUND: There are few concise tools to evaluate dietary habits in men with prostate cancer in Canada. OBJECTIVE: The aim was to develop a short food-frequency questionnaire (SFFQ) in a cohort of prostate cancer patients. METHODS: A total of 130 men with prostate cancer completed the SFFQ and a validated comprehensive food-frequency questionnaire (CFFQ). Both questionnaires were administered at baseline and 6 mo later. RESULTS: We found good correlation between the SFFQ and the CFFQ for seafood, dairy, egg, fruits, potatoes, grains, soft drinks, and processed meat (Spearman rank correlation >0.5). Moderate correlation was found for meat, sweets, vegetables, protein, and carbohydrates (Spearman rank correlation: 0.3-0.5). We found a weaker correlation for total fat measured by SFFQ and CFFQ (Spearman rank correlation <0.3). There was adequate reproducibility during the 6-mo follow-up among all food groups and nutrients, with the exception of meat. CONCLUSIONS: Our SFFQ can be considered an appropriate tool to be used for measuring the habitual dietary intake of prostate cancer patients. This trial was registered at www.clinicaltrials.gov as NCT03127631.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".